A typical design procedure for model predictive control or control performance monitoring consists of: identification of a parametric or nonparametric model; derivation of the output predictor from the model; design of the control law or calculation of performance indices according to the predictor. Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind the work presented in this book forms a new design paradigm that eliminates the first and second step of the above design procedure. The subjects treated include: closed-loop subspace identification; multivariate control performance assessment. The approach presented in this book can be considered to be "data-driven" in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained directly from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is greatly simplified and the modelling error caused by parameterization is eliminated